Determination of structures, such as organs, in a patient and extraction of the shape of a structure is essential to many medical imaging applications, such as diagnostic imaging, image-guided surgery, or image-guided radiation therapy. In such applications, a target structure or target structures need to be determined from an image, such as a computed tomography (CT) image, of the patient. The determination of the target structure(s) in the patient is usually known as structure contouring or segmentation. Although manual contouring by human experts, also referred to as raters, is still a common approach for high quality segmentation in clinics, manual contouring is tedious and time-consuming, and may suffer from large intra- and/or inter-rater variations.
Automated segmentation of images can be challenging due to noises and other artifacts, as well as limited image contrast for many soft-tissue structures. In recent years, atlas-based auto-segmentation (ABAS) techniques have shown promise as a solution. The ABAS includes performing segmentation of a subject image using one or more previously-segmented images, such as segmented images from previously treated patients or from previous treatments of the same subject patient. The previously-segmented images together with their annotations, e.g., structure label maps or structure surfaces, are referred to as atlases. By aligning an atlas image to a new subject image obtained from the subject patient through image matching, also referred to as image registration, an image transformation is computed. Structure labels for the subject image are produced by mapping structure labels defined on the atlas to the subject image using the computed image transformation.
The accuracy of ABAS usually relies on the quality and quantity of atlas images used. For example, multiple atlases can be used during the ABAS process to provide redundancy. On the other hand, atlas images showing similar underlying objects of those in the subject image may also help improve accuracy in labeling the subject image. The disclosed methods and systems are designed to further improve the accuracy of conventional ABAS for image segmentation.